#58 Explainable AI by Felipe Flores

Felipe Flores.jpg

Today we have a different type of episode, this is a presentation that Felipe did at the Chief Data and Analytics Officer Conference in Canberra, and it is on explainable AI. First, Felipe explains how Amazon used a secret AI recruiting tool that had a bias against women. Also, the U.S. government used an algorithm predicting how likely people in the criminal justice system would reoffend. What they found is that it targeted specific racial groups. The algorithm isn’t racist or sexist, the data is.

Regarding job applications, as your company scales up, the need to automate the process of looking at the applications becomes necessary. Sometimes, bias will creep into the automated decision-making algorithm. The bias can even be narrowed down to the person’s name. For example, somebody with name Felipe might get scored lower than somebody with the name Tyler. Lean into the inequality and predict the bias. You can plug in the CV information, and ask the algorithm to predict the person’s race and gender. Then, find out what key inputs they are flagging to determine this and remove them from the algorithm.

Then, Felipe explains how algorithms can tackle unstructured data approaches. When discussing images, an algorithm was able to correctly identify a wolf from a husky 5 out of 6 times. However, when uncovering how the algorithm determined which was which, it was merely looking at if the animal was in the snow or not. If the picture had snow in it, then it must be a wolf. To determine how this algorithm was functioning, Felipe used LIME - Local Interpretable Model-Agnostic Explanations. It works for classifications and came out of a study from MIT. Later, Felipe discusses using EL15 and how transparency is essential for the public to understand how the algorithms could affect them.

Enjoy the show!

We speak about:

  • [03:40] Large companies and their biases

  • [05:40] Racism and sexism is in our data

  • [08:45] Uncovering inputs of the bias

  • [10:45] Unstructured data approaches

  • [14:30] Using ELI5

  • [19:20] The right to an explanation


  • “We teach our algorithms on how to replicate our decisions.”

  • “The algorithms show the inequality that we have in the world today.”

  • “Explainable AI is more ethical in the sense that it is more transparent.”

  • “Explainable AI helps us avoid blunders and informs us how the algorithm perceives the data.”

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Felipe Flores is based in Melbourne, Victoria, Australia.

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#31 Scott Wilson - Founder & CEO

Ep31 Scott Wilson - Sandringham, VIC.jpg

Scott started his career pushing trolleys at Woolworths. In his career he rose to management levels in retail with Woolworths, consumer goods with Kraft Foods, Fonterra SPC and PZ Cussons, then in media with 21st Century Fox. He then became the CEO of iSelect, a role he left earlier this year to start his own AI company Wilson AI.

We speak about:

* Focus on customer needs

* Digitising industries to access more data

* Helping companies in multiple industries to begin their data analytics journey

* How to differentiate your company when competitors have access to the same data

* How to overcome being "data rich but insight poor"

* Changing industry power dynamics through data

* Creating new teams to create value from data

* The importance of storytelling in data science

* Defining objectives with your data analytics communication

* Educating industries to use data more effectively

* Understanding costs & priorities across the value chain to make better decisions

* Eliminating your biases when dealing with customers

* Process re-engineering & AI

* How to think outside of the building

* How to start an AI company

* The importance of translating between business and technical

* How to connect data science and the boardroom

* The importance of data science education in an organisations journey

* How to achieve a wider spread adoption of AI

* Focusing on cost & revenue with data science for maximum impact

* Resist the urge to boil the ocean

* The role of a CEO in a publicly listed company

* Focusing on the top 3 business priorities

* Productionising AI & monitoring unintended consequences

Scott is based in Sandringham, Victoria, Australia

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#25 Ben Taylor - Chief Al Officer & Cofounder

Ep 25 Ben Taylor - Provo Utah Area.jpg

Ben started his career as a chemical engineer. He developed an interest for computer vision early on. He worked for Intel, then at a hedge fund and then became the Chief Data Scientist at HireVue. A couple of years ago he started his own AI startup called Ziff.ai where he's is building a Deep Learning platform for product visionaries and software engineers.

We speak about:

* How computers amplify us

* What it looks like to start your own AI company

* How to switch programming languages

* Downsides of Google's tensorflow

* What industry expects from data science

* How to deliver value with ML

* How to pick ML projects to tackle

* Eliminating bias in AI applications

* AI powered job interviews of the (near) future

* Topic discovery with DL

* AI warfare in business

* What is a Hive Mind and how it works

* Future health care assessments at home

* AI is cute until it's scary

* The importance of passion and obsession in data science

Articles by Ben on Linkedin:

This is Why Your Data Scientist Sucks:


The Al War Machine: Our Darkest Day


The Al War Machine: The Hive Mind


Getting That Data Science Job


From 0 to $100K+ data science job in 6 months


Ben is based in the Provo, Utah Area

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!